Dark Synthetic Vision: Lightweight Active Vision to Navigate in the Dark

Joowan Kim, Myung-Hwan Jeon, Younggun Cho, Ayoung Kim

Abstract: Overcoming illumination variance is a critical factor in vision-based navigation. Existing methods tackled this radical illumination variance issue by proposing camera control or high dynamic range (HDR) image fusion. Despite these efforts, we have found that the vision-based approaches still suffer from overcoming darkness. This letter presents real-time image synthesizing from carefully controlled seed low dynamic range (LDR) image, to enable visual simultaneous localization and mapping (SLAM) in an extremely dark environment (less than 10 lux). Unlike existing methods, we elaborately select the seed LDR image for HDR fusion to secure interframe consistency, which is important in visual navigation. After selecting the seed image by camera control, we exploit camera response function (CRF) to synthesize HDR images in real-time without requiring GPU. We validate the algorithm via two extremely dark environments, an indoor environment without light and an outdoor night. In both test scenarios, the proposed method enabled reliable visual SLAM even when the light was limited.